FITNESS TRACKER

INTRODUCTION and PROBLEM STATEMENT

The aim of this report is to analyze the data collected from a Fitness tracker, which is a wearable device designed to track various aspects of an individual’s physical activity, such as steps taken, distance traveled, calories burned, and heart rate. The data analyzed in this report was collected over a specified period of time and provides insights into the physical activity patterns of 33 individuals wearing the device.

This report will present the findings of the data analysis, including trends and patterns in the individuals’ physical activities, as well as any correlations between different aspects of the data. The report will also highlight any notable achievements or changes in the individuals’ physical activities during the specified period of time.

This report is distributed in three sections.

  • Section 1: Analysis of users’ daily activities

  • Section 2: Analysis of steps taken and sleep patterns

  • Section 3: Analysis of Calories burnt. Overall, this report aims to provide a comprehensive overview of users’ physical activity as recorded by their Fitness tracker, and will serve as a valuable tool for understanding their fitness level, tracking their progress, and identifying areas for improvement.

SECTION 1:

Analysis of users’ daily activity

Q. What is the distribution of users’ daily activity in a week?

Insight: The above visualization plot shows all the 33 users’ daily activity by steps and we can observe that the difference between the days of the week is minimal.

Q: What is the percentage of users that are lightly active (steps in minutes)?

Insight: This visualization chart shows the distribution of users’ daily activity(steps) in minutes. We observed that only 1.12% of users’ do moderate activity and only 1.74% of the users’ do heavy activity daily. Large amount of users (81.3%) are sedentary. 15.8% of the users are lightly active.

Q: What is the percentage of users that are lightly active (calories burnt)?

Insight: Here, we are analyzing the percentage of calories burnt by the users with different active levels. And it is observed that 57.2% are sedentary based on the calories that they burnt. 32.4% of the users are lightly active.

SECTION 2 :

Analysis of the steps taken and sleep patterns of User

Q. What is the distribution of Steps of users throughout the day?

Insight : The above visualization plot shows the average hourly steps of all the 32 users in a particular day. From what we can observe from this plot is that all the users in average are more active between the time of 8am and 7pm. Majorly step count increases between the time of 5pm and 8pm.

Q. What is the sleep distribution of users throughout the day?

Insight : Now here, we our analysing the sleep distribution of all user in minutes. From this visualization we can determine how many users have a sound sleep. Good and healthy sleep range must be between 7hr to 9hr range i.e, between 630 and 720 minutes.

However the above visualization shows that majority of users average minutes of sleep, following a normal distribution. A majority of users sleep approximately 320 to 530 minutes.

Getting deeper into the analysis, we come up with this question.

Q. Is there a correlation between sleep time and distance travelled by the fit bit users?

Insight : Now we want to determine whether the step counts has a correlation with the sleep time and how basically it affects the sleep time of the users. To determine this, correlation we have decided to plot between the total distance travelled by the users in a day with total sleep time of user in minutes.

What we can infer from this plot is that covering a greater distance doesn’t necessarily mean that the user is going to have a better sleep (on average).

Let’s confirm this theory with the next question.

Q. Does the amount of steps taken affect the sleep quality of the users?

## `summarise()` has grouped output by 'sleep_quality'. You can override using the
## `.groups` argument.

Insight: Now as mentioned earlier we know that the good quality of sleep have the time range between 7hr to 9hr.

As we could not infer much from the earlier graph. We here are trying to focus on the best sleep quality and what we infer is that on average the best sleep is achieve when the total steps during the day are between 5000 steps and 14,999 steps. The optimal steps range to have a good sleep quality is between 5,000 and 9,999.

SECTION 3:

Analysis of Calories burnt by users

Burning calories through exercise is one of the crucial part for the healthy lifestyle. Fitness tracker are accurate enough to measure calorie burns throughout the day. The number of calories burnt for each user is definitive to understand users’ health habits as well as tracking the progress of their regimen.

Q1. Does burning calories have correlation with the type of users based on how active they are?

Insight: The users are categorized into sedentary, lightly, fairly and very active based on the minutes of their activity throughout the day. A box plot is a suitable graph to compare these user types based on median, quartiles and outliers.The average calories burnt by the users gradually increases starting from sedentary to very active individuals. Surprisingly, light and fairly active individuals do not show much difference in burning the calories however, the median calorie burnt for fairly active individuals is less than the lightly active individuals. Very active individuals are almost burning 3000 calories on an average per day.

Q2. Does burning calories have correlation with the number of steps/distance that users travel?

Insight: The users are categorized based on the distance traveled (Less than 5 Miles, 5 to 7 miles and more than 7 miles) as well as Number of steps taken (less than 6000, 6000 to 10000 and more than 10000). Based on the summary values, this Boxplot shows the calories burned by the three categories of steps, which are then faceted by the three categories of distance traveled. It determines which component is more crucial to the number of calories burned, and it’s interesting to note that the most calories are burned by the “6,000 > 10,000 Steps” and “> 7 miles,” which would suggest some sort of running activity that allows participants to cover more distance with fewer steps.

It is also intriguing to see that, the similarity between the “> 10,000 Steps” in the mean distance section and the “6,000 Steps” in the low distance section, which supports the notion that speed is the primary determinant of calories burned.

Q3. What hour of the day is the most popular in users to burn calories?

Insight: Everyday, 5 pm to 7 pm is the most common time of the day when users tend to burn more calories. Users burn more than 120 calories per hour during this time. Other than that, the calorie burning is seemed to be steady throughout the day.There is a dramatic drop in the calorie burning activities since evening and keeps decreasing till midnight. During the night time, the calorie expense is steady at around 70 calories per hour.

CONCLUSION and SUMMARY:

  • Amount of distance traveled by the user does not affect their sleep quality i.e, covering a greater distance doesn’t necessarily mean that the user is going to have a better sleep (on average).

  • The other thing we have noticed is that slight amount of activity just before going to bed can help achieve a better sleep. We can use this insight and add a feature which can help users set a reminder of doing some amount of activity so that they can reach that optimal steps range between 5,000 and 9,999 which could help user achieve a better sleep quality.

  • The 33 users with different active levels have very minimal difference in the total number of steps in a week.

  • Most of the users are sedentary with respect to steps in minutes and calories burnt.

  • Counting calorie intake and burn are one of the prominent task for the health conscious people. Therefore we tried to get insights from the data on what are the factors affecting the calorie burn and how effective they are. Its been observed that active individuals tend to burn more calories than the individuals who majorly do sedentary activities. Also, Running is more effective in calorie burning than walking.